Cytological imaging of medical specimens is a tedious but crucial tool for medical analyses. Automated cytological imagers have been developed to meet the need for more uniform cytological image analyses. Automated cytological imagers do not vary as greatly in their interpretations of slides, are less subject to fatigue, and can provide much greater throughput as compared to humans.
Several previously developed and some currently available automated systems are used in conjunction with additional human analysis, and are used to increase the number of samples assayed and to lessen the fatigue experienced by the human analyst. Automated screeners can be used to select from each sample, objects for further human review. This method can increase the sensitivity of such assays, as the machine may more readily and economically identify those objects of interest in each sample to be analyzed by a human.
However, automated imagers are limited by the sample and data provided to them and by their programming. Additionally, for computational reasons, imagers typically use monochromatic, black and white images for their analyses, whereas the sample itself may provide a great range of spectral data and other information, particularly for cytologically stained samples.
For example, in the automated image analysis of pap-stained samples, the classification of abnormal objects in a conventional automated screening system can be complicated by the presence of normal metaplastic cells and other confounding objects. Some imaging systems identify cells of interest in pap-stained specimens on the basis of their optical density, as they or their nuclei may appear “darker” (more optically dense) and/or larger than do normal cells in the specimen. Metaplastic cells in the stained specimen also have dark cytoplasms and consequently reduced nuclear:cytoplasmic contrast that may contribute to errors in measurement. The metaplastic cells can be quite numerous on a slide, while abnormal cells may appear infrequently, and thus automated imagers can undesirably select the metaplastic cells for human review as they appear equivalently dark to the imager but are much more numerous than the abnormal cells. The false selection rate of the frequently occurring but disease-negative metaplastic cells by the imager thus limits accurate disease detection.